Coughprint: Distilled Cough Representations From Speech Foundation Model Embeddings
Bibliographic record
Abstract
Supportive smart home systems with integrated sensors capable of measuring cough frequency and severity can support independent living and aging in place by helping monitor the state of acute and chronic health conditions. Previously, we showed that embeddings from speech foundation models are effective cough representations for a range of cough measurement applications. While powerful, the large compute and memory requirements of these models prevents them from being deployed in embedded smart sensors. In this work we use knowledge distillation to train edge-compute focused student models, making them feasible for the measurement, identification, and classification of cough sounds directly in the smart home. This embedded processing avoids the privacy and security concerns associated with transmission and storage of sensitive audio recordings in the cloud. We show that the student networks preserve the universal cough representation capabilities of the teacher, even generalizing to unseen classes such as speech, allowing the same network to be used for multiple downstream applications without any task-specific fine-tuning. A student network based on a 14-layer variant of ResNet achieved the highest aggregate quality score across the downstream evaluation tasks, even outperforming the foundation model teacher on certain tasks despite having over 200× fewer parameters. Linear classification on the embeddings from the proposed student network achieves strong performance on a diverse set of cough measurement tasks, scoring 98.3% on cough/non-cough discrimination, 90.3% on human sound classification, 94.8% on cougher verification, 84.4% on cougher identification, and 87.8% on wet/dry cough classification.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".